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Machine Learning Researcher – PhD Graduate (Asia)

Citadel Securities
🇭🇰Hong Kong🇸🇬SingaporeStudentFull-timePosted September 22, 2026

About the Role

Citadel Securities, a leading global market maker, is seeking a Machine Learning Researcher to join its quantitative research team. The team aims to leverage and tailor state-of-the-art machine learning and AI algorithms to modernize the quantitative trading industry.

  • Use statistics, machine learning or AI (e.g. deep learning, NLP) to extract patterns from various kinds of datasets through innovative and rigorous research
  • Implement algorithms in high-quality code
  • Work with large data sets, including unconventional and unstructured data sources
  • Back-test models and document research findings

We're looking for extraordinary and highly motivated researchers who are excited about solving challenging problems and iterating in a fast-paced environment. Opportunities are available in Singapore and Hong Kong.

Responsibilities

  • Use statistics, machine learning or AI (e.g. deep learning, NLP) to extract patterns from various kinds of datasets through innovative and rigorous research
  • Implement algorithms in high-quality code
  • Work with large data sets, including unconventional and unstructured data sources
  • Back-test models and document research findings

Requirements

  • PhD degree in mathematics, statistics, physics, computer science, or another highly quantitative field
  • Advanced training and strong research track record in statistics, machine learning, AI, or another highly quantitative field
  • Prior experience working in a data driven research environment
  • Detail oriented and can deep dive into the data
  • Hands on programming experience in scripting (e.g. Python) and/or compiled languages (e.g. C++)
  • A background demonstrating strong problem-solving skills
  • An ability to communicate advanced concepts in a concise and logical way
  • Proficiency in creating and using algorithms to meticulously investigate and work through large data or error-checking problems